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Smarter Decisions - The Intersection of Internet of Things and Decision Science

You're reading from   Smarter Decisions - The Intersection of Internet of Things and Decision Science A comprehensive guide for solving IoT business problems using decision science

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Product type Paperback
Published in Jul 2016
Publisher Packt
ISBN-13 9781785884191
Length 392 pages
Edition 1st Edition
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Author (1):
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Jojo Moolayil Jojo Moolayil
Author Profile Icon Jojo Moolayil
Jojo Moolayil
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Table of Contents (10) Chapters Close

Preface 1. IoT and Decision Science FREE CHAPTER 2. Studying the IoT Problem Universe and Designing a Use Case 3. The What and Why - Using Exploratory Decision Science for IoT 4. Experimenting Predictive Analytics for IoT 5. Enhancing Predictive Analytics with Machine Learning for IoT 6. Fast track Decision Science with IoT 7. Prescriptive Science and Decision Making 8. Disruptions in IoT 9. A Promising Future with IoT

A Brief Introduction to Machine Learning

Machine learning is not a very well-defined term in the industry. There is a variety of definitions available in multiple textbooks and e-resources. The general difference between statistical modeling and machine learning is a much talked about topic but is still a very ambiguous term. At a high level, we can call machine learning an advanced layer in the predictive stack of decision science; an area where powerful algorithms and techniques use data to learn patterns and relationships to predict an outcome.

We started our predictive journey using statistical modeling. You learned how to implement and use various statistical models such as linear regression, logistic regression, and decision trees. We'll now try solving the same problem using more advanced algorithms that will give us better results. Before we start, we still want to know: what is machine learning and how is it different from statistical modeling?

In a single sentence, machine...

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